code: def test_SeparateSpeech(
      config_file,
      batch_size,
      input_size,
      segment_size,
      hop_size,
      normalize_segment_scale,
  ):
      separate_speech = SeparateSpeech(
          train_config=config_file,
          segment_size=segment_size,
          hop_size=hop_size,
          normalize_segment_scale=normalize_segment_scale,
      )
      wav = torch.rand(batch_size, input_size)
      separate_speech(
          wav, fs=8000, enroll_ref1=torch.rand(batch_size, 16000)
      )
  
file_path: espnet/test/espnet2/bin/test_enh_tse_inference.py
imports: from argparse import ArgumentParser
  from pathlib import Path
  import pytest
  import torch
  import yaml
  from espnet2.bin.enh_tse_inference import SeparateSpeech, get_parser, main
  from espnet2.enh.encoder.stft_encoder import STFTEncoder
  from espnet2.tasks.enh_tse import TargetSpeakerExtractionTask
  from espnet2.utils.get_default_kwargs import get_default_kwargs
  from espnet2.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
label_desc: Use image node_id 4 for calling a global function with example usage: test_SeparateSpeech(config_file, batch_size, input_size, segment_size, hop_size, normalize_segment_scale) without return types